The same ideas also found application in practical tasks,
such as handwritten digit recognition [1]},
where robust generalisation to small deformations was vital.
Despite these strengths and the reduced number of free parameters,
effective training of such models still required
large amounts of data, which motivated the development of
datasets such as ImageNet [2]}.
Given these extensive collections of natural images,
it has become possible to train neural models of object recognition
with impressive test set performance
[3]}, [4]}, [5]}.
